Type 2 Diabetes

Develop a novel statistical software package that can help researchers identify new biomarkers and drug targets using already existing genomic data from Type 2 Diabetes Mellitus patients.

The BALDER team used a mathematical approach known as Multi-Trait Bayesian Linear Regression to developa software package, where the user can input genes of interest and select different filters based on interest. The BALDER software package helps the user interpret existing data and see new connections - also new biomarkers and drug targets.

In the BALDER project, the team has taken advantage of the vast amount of genetic data from Tyep 2 Diabetes Mellitus patients that is already out there. These data has been used as a case study to demonstrate the usefulness of the software package. However, researchers can use the software for basically any disease of interest.

Colon cancer

To understand the process of metastasis in colon cancer and thereby identify new biomarkers and potential drug targets.

The BIOMETSCO team investigates tumor tissue samples from colon cancer patients - with a special interest in the invasive tumor front. First, an overall "bulk" analysis is performed to compare two different tissue samples (e.g. healthy vs disease) and identify genes of interest. The second step is spatial transcriptomics to find out which cells express the genes of interst and where these cells are located in the tissue. Lastly, the metastasis-related genes are analyzed in knockdown experiments.

This approach can in principle be applied to many other disease areas.

Psychiatric disorders

To find biomarkers for different psychiatric disorders that can help clinicians diagnose patients and find the most effective individual treatment – and potentially also develop patient-specific treatments.

In the BioPsych project, the reasearch team used small brain fragments from the Danish Brain Collection, Risskov to identify sex-specific RNA biomarkers for different psychiatric disorders such as depression, bipolar disorder and schizophrenia. The brain samples were collected from psychiatric patients in Denmark between 1945-1982 and the patients have not been treated with the modern medicines used today. Therefore, the BioPsych team were able to compare the "natural state" of brains between different psychiatric disorders. This allowed the team to look for biomarkers that can distinguish between the different psychiatric disorders.

Skin diseases

To better mimic the in vivo environment of cells in cell culture assays to obtain in vitro experimental results that translate better to a human setting.

The CELPPLUS team has developed a method for better mimicking the natural environment in cell culture assays by using nanotechnological strategies: Proteins from the cells' natural surroundings were placed in a nanopattern on glass plates as the bottom of the traditional cell culture wells. By mimicking the in vivo environment, the cells can behave more similarly to how they would behave in the human body.

The CELPPLUS project used keratinocytes as a case study, but the methods of mimicking the natural surroundings of cells can be used for basically all cell lines that are studied in the lab.

Chronic Kidney Disease (CKD)

To develop a model system, which better reflects human chronic kidney disease.

The FRIGG model system can serve as a platform for identification of potential biomarkers and also  as an important step in identifying drug targets and testing potential drug candidates.

The large interdisciplinary FRIGG project team used fresh human kidney slices to establish a platform, which models the different stages of human chronic kidney disease (CKD). Fresh biopsies from both healthy and CKD patients were delivered directly from the operation theater to the research lab. The biopsies were then cut into thin slices and placed in different culture media to induce the distinct phenotypes of kidney tissue observed at different disease stages. The slices can be analyzed by e.g., standard histological methods and spatial transcriptomics.

Parkinson's disease

To identify immune-related biomarkers in the blood of people with Parkinson's disease that will enable clinicians to easily diagnose and stratify and follow patients based on their presence. The IMPAD project also aimed to identify novel targets for immunomodulation that can be used for treating the disease.

The IMPAD team used blood samples from both healthy controls and well-characterized people with Parkinson’s disease to identify changes in the immune system at different disease stages and subtypes. The blood samples were subjected to both cellular and protein  analyses. In this way, the IMPAD team could both show how the neruons are at different stages of Parkinson's disease, and identify blood biomarkers that are only present during disease or at specific stages of the disease. Taken together, the IMPAD team can show how the prescence of blood biomarkers evolves in association with the neuronal changes.

Chronic kidney disease

To identify metabolites that can predict progression of CKD - and to determine if an interplay between metabolites and proteins within the kidneys drive CKD progression.

The KidDO project focused on changes in metabolites in the kidney during chronic kidney disease. The team used different omiics strategies to identify metabolites and proteins that were altered at different stages of CKD, and to see how they interacted and affected the progression of the disease.

The KidDO team also evaluated five different animal models and compared them to human samples using machine learning strategies, with the hope of identifying which model system most closely resembles the human disease

Neurological disorders

To develop a high throughput and reproducible human organoid platform, which can be used to investigate mechanisms related to neurological disorders and for the validation of new drugs.

The MiCO Platform team uses human induced pluripotent stem cells to develop micro brains.The micro brains, which are microscopic models of either the human fore-, mid- or hindbrain, can be generated from any patient cell sample and they are both fast and easy to produce and their small size and high reproducibility makes them ideal for drug candidate screening.

Liver disease (NAFLD/NASH)

To identify blood biomarkers in patients suffering from MASH. The biomarkers can potentially help diagnose patients, predict disease development and to monitor treatment responses.

The oLIVER project team is using an approach called APTASHATE. By mixing a blood sample millions of small RNA biosensors, the team can generate a digital 'picture' of the blood sample's composition. By 'taking pictures' of different blood samples, the APTASHAPE approach can help researchers find out, which molecular changes are taking place during disease.

Cancer

To increase the understanding of the physiological effects of cell penetrating proteins (CPPs) and thereby eventually enable intracellular drug delivery using CPPs.

The P2P CPP team set-up an interative approach for testing CCP-drugs. First the CPP-drug’s effects were analyzed in electrophysiological and vasodilation assays. Here, the P2P CPP team analyzed ion currents and contractility arteries from both rodents and humans. As a last step, the observed effects of the CCP-drugs were compared to the in vivo effect in mice.

Atherosclerosis

To find new drug targets in relation to smooth muscle cells for the treatment of atherosclerosis.

The THOR project team started out by identifying human genes linked to atherosclerosis before they filtered out the genes that were related to cholesterol. From this list, the genes that were expressed predominantly in smooth muscle cells were selected for knockdown studies in cell culture assays with smooth muscle cells steered into atherosclerosis-relevant phenotypes.